Abstract

When water resource systems are not under control, the consequences can be devastating. In the United States alone, flood damage cost approximately $1.5 billion annually. These losses can be avoided by building more reservoirs to hold the flood waters, but such construction is very expensive, especially because reservoirs have already been built on the best sites. A better and less expensive alternative is the development of more effective management methods for existing water resource systems, which commonly waste approximately 20 percent of their capacities through mismanagement.

Statistical models first appeared in hydrology at the beginning of the 1970s. Hydrologists began to use the techniques of time series analysis and system identification in their models, which seemed to give better results than the earlier, deterministic simulation models. In addition, real-time control of water resources was being developed at the practical level and on-line measurements of rainfall and runoff from a catchment were becoming available. The conceptual models then in use could not take advantage of measurements from the catchment, but on-line measurements now allow an operator to anticipate flood waters upstream or a water shortage downstream.

This book contains selected papers from a workshop devoted to the consolidation of international research on statistically estimated models for real-time forecasting and control of water resource systems. The book is divided into three parts. The first part presents several methods of forecasting for water resource systems: distributed lag models, maximum likelihood identification, nonlinear catchment models, Kalman filtering, and self-tuning predictors. The papers in the second part present methods for controlling stream quality and stream flow, and the third part describes forecasting in the United States, the United Kingdom, and Poland.